Knowledge Graph Datasets for Recommendation

In the era of daily information overload, personalized retrieval applications represent a crucial solution to provide suggestions to users. The research and industrial community have devoted an unprecedented effort to propose approaches and architectures to extract relevant and tailored information from every shred of knowledge. Inspired by the advances in knowledge graph, Graph Convolutional Networks, Link Prediction, and Recommender Systems research, this study aims to meet their cutting-edge research needs of holistic datasets by largely expanding the information available for two well-known recommendation datasets in the book and movie domains, i.e., LibraryThing and MovieLens 25M. We collect the associated knowledge graphs (KGs) for each of them and publish a mapping for each item in the two datasets with the corresponding entities in the original Wikidata, DBpedia, and Freebase KGs. Our work is available at https://github.com/sisinflab/Augmented-and-Linked-Open-Datasets-for-Recommendation.